corkdist                package:ade4                R Documentation

_T_e_s_t_s _o_f _r_a_n_d_o_m_i_z_a_t_i_o_n _o_n _t_h_e _c_o_r_r_e_l_a_t_i_o_n _b_e_t_w_e_e_n _d_i_s_t_a_n_c_e_s _a_p_p_l_i_e_d _t_o '_k_d_i_s_t' _o_b_j_e_t_c_s

_D_e_s_c_r_i_p_t_i_o_n:

     The mantelkdist and RVkdist functions apply to blocks of distance
     matrices the mantel.rtest et RV.rtest functions.

_U_s_a_g_e:

     mantelkdist (kd, nrepet = 999)
     RVkdist (kd, nrepet = 999)
     plot.corkdist (x, whichinrow = NULL, whichincol = NULL, 
        gap = 4, nclass = 10, coeff = 1,...)

_A_r_g_u_m_e_n_t_s:

      kd: a list of class 'kdist'

  nrepet: the number of permutations

       x: an objet of class 'corkdist', coming from RVkdist or
          mantelkdist

whichinrow: a vector of integers to select the graphs in rows (if NULL
          all the graphs are computed)

whichincol: a vector of integers to select the graphs in columns (if
          NULL all the graphs are computed)

     gap: an integer to determinate the space between two graphs

  nclass: a number of intervals for the histogram

   coeff: an integer to fit the magnitude of the graph

     ...: further arguments passed to or from other methods

_D_e_t_a_i_l_s:

     The 'corkdist' class has some generic functions 'print', 'plot'
     and 'summary'. The plot shows bivariate scatterplots between
     semi-matrices of distances or histograms of simulated values with
     an error position.

_V_a_l_u_e:

     a list of class 'corkdist' containing for each pair of distances
     an object of class 'randtest' (permutation tests).

_A_u_t_h_o_r(_s):

     Daniel Chessel chessel@biomserv.univ-lyon1.fr
      Anne B Dufour dufour@biomserv.univ-lyon1.fr

_E_x_a_m_p_l_e_s:

     data(friday87)
     fri.w <- ktab.data.frame(friday87$fau, friday87$fau.blo, 
         tabnames = friday87$tab.names)
     fri.kc <- lapply(1:10, function(x) dist.binary(fri.w[[x]],10))
     names(fri.kc) <-  substr(friday87$tab.names,1,4)
     fri.kd <- kdist(fri.kc)
      fri.mantel = mantelkdist(kd = fri.kd, nrepet = 999)
      plot(fri.mantel,1:5,1:5)
      plot(fri.mantel,1:5,6:10)
      plot(fri.mantel,6:10,1:5)
      plot(fri.mantel,6:10,6:10)
     s.corcircle (dudi.pca(as.data.frame(fri.kd), scan = FALSE)$co)
     plot(RVkdist(fri.kd),1:5,1:5)

     data(yanomama)
     m1 <- mantelkdist(kdist(yanomama),999)
     m1
     summary(m1)
     plot(m1)

